Mining of Massive Datasets 2025 pdf epub mobi 电子书

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Mining of Massive Datasets 2025 pdf epub mobi 电子书著者简介

Jure Leskovec is Assistant Professor of Computer Science at Stanford University. His research focuses on mining large social and information networks. Problems he investigates are motivated by large scale data, the Web and on-line media. This research has won several awards including a Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, Okawa Foundation Fellowship, and numerous best paper awards. His research has also been featured in popular press outlets such as the New York Times, the Wall Street Journal, the Washington Post, MIT Technology Review, NBC, BBC, CBC and Wired. Leskovec has also authored the Stanford Network Analysis Platform (SNAP, http://snap.stanford.edu), a general purpose network analysis and graph mining library that easily scales to massive networks with hundreds of millions of nodes and billions of edges. You can follow him on Twitter at @jure.

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发表于2025-04-10

Mining of Massive Datasets 2025 pdf epub mobi 电子书

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麻烦支那猪以后翻译外文书籍，先找个稍微懂行的把书看一遍行吗！鉴于中文翻译缩水不准的情况，本掉千辛万苦找来英文原版，一看到目录，本屌就硬了，尼玛作者太牛逼了！最新补充一句，话说如果这本书的名字叫做类似《数据挖掘基础》的话，本屌绝壁不喷它。本来就是基础的基...

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本来是计划读英文版《Mining of Massive Datasets》的，但看到打折，而且译者在序言中信誓旦旦地说翻译的很用心，就买了中文的。结果读了第一章就读不下去了，中文表述太烂了，很多句子让人产生无限歧义，磕磕绊绊，叫人生厌。因此决定再次放弃这样的中文翻译书。

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内容是算法分析应该有的套路, 对于Correctness, Running Time, Storage的证明; 讲得很细, 一个星期要讲3个算法, 看懂以后全部忘光大概率要发生. 要是能多给些直觉解释就好了. Ullman的表达绝对是有问题的, 谁不承认谁就是不客观, 常常一句话我要琢磨2个小时, 比如DGIM算法有一...

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看到好多人说这本书是大纲，是目录，没啥内容，讲的浅。那就对了。本书是Stanford CS246课程MMDS使用的讲义，还有配套的Slides和HW，所以观看本书请配套课程进行学习，同时coursera上也有配套的课程。 See more detail: http://www.mmds.org/

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出版者:Cambridge University Press

作者:Jure Leskovec

出品人:

页数:476

译者:

出版时间:2014-12-29

价格:USD 75.99

装帧:Hardcover

isbn号码:9781107077232

丛书系列:

图书标签: 数据挖掘计算机机器学习 Data Coursera CS 数据分析软件工程

Mining of Massive Datasets 2025 pdf epub mobi 电子书图书描述

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

Mining of Massive Datasets 2025 pdf epub mobi 电子书

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